GISC 4360K - 2019 Spring

Lectures

1. Lecture 1: Python basics
2. Lecture 2: What is digital image processing?
3. Lecture 3: Digital image fundamentals
   1. Homework 1: Wavelength and image storage
4. Lecture 4: Zooming, shrinking, and grayscaling of images
   1. Homework 2: Bilinear interpolation
5. Lecture 5: Relationships between image pixels
   1. Homework 3: Shortest 4-path
6. Quiz 1
7. Lecture 6: Image transformations and slicing
8. Lecture 7: Logic and arithmetic image operations
   1. Exercise 1: Shadow enhancement using logic operations
   2. Exercise 2: Noise reduction using averaging
   3. Homework 4: Noise reduction using averaging
9. Lecture 8: Histogram equalization of images
10. Lecture 9: Local image enhancement and image-smoothing spatial filters
    1. Exercise 3: Shadow enhancement using local enhancement
    2. Exercise 4: Smoothing
    3. Exercise 5: Noise reduction
    4. Homework 5: Local image enhancement
11. Lecture 10: Image-sharpening spatial filters
    1. Exercise 6: Edge extraction
    2. Exercise 7: Sharpening 1
    3. Exercise 8: Sharpening 2
    4. Exercise 9: Sharpening 3
    5. Homework 6: Image sharpening filter
12. Quiz 2
13. Lecture 11: Fourier series
14. Lecture 12: Introduction to the discrete Fourier transform
    1. Exercise 10: One-dimensional image
    2. Exercise 11: Two-dimensional image
15. Lecture 13: Introduction to frequency-domain filtering
    1. Exercise 12: Plotting $F(u)e^{2i\pi ux/M}$
    2. Exercise 13: Reading the Fourier spectrum
    3. Exercise 14: Fourier transform components
16. Lecture 14: Frequency-domain filtering
    1. Exercise 15: Fourier transform
    2. Exercise 16: Ideal low-pass filter
    3. Exercise 17: Gaussian low-pass filter
    4. Exercise 18: Gaussian high-pass filter
    5. Homework 7: Fourier transform
17. Quiz 3
18. Lecture 15: Color fundamentals
    1. Homework 8: Color interpolation
19. Lecture 16: Color models
    1. Exercise 19: RGB-to-CMY conversion
    2. Exercise 20: RGB-to-HSI conversion
    3. Exercise 21: HSI-to-RGB conversion
    4. Homework 9: Color model conversions
20. Lecture 17: Full-color image processing
    1. Exercise 22: RGB-to-CMY conversion in ArcGIS Pro
    2. Exercise 23: RGB-to-HSI conversion in ArcGIS Pro
    3. Exercise 24: HSI-to-RGB conversion in ArcGIS Pro
    4. Exercise 25: Smoothing
    5. Exercise 26: Sharpening
    6. Exercise 27: Extracting clouds using color segmentation
    7. Exercise 28: Removing clouds using color segmentation
21. Quiz 4